Non-Communicable Disease Burden (NCD)

# Load necessary packages
pacman::p_load(tidyverse, knitr, here, plotly)

# Import the non-communicable diseases data
data_ncd <- read_csv(here("data/burden-of-disease-ncd.csv"))
## Rows: 8010 Columns: 4
## ── Column specification ────────────────────────────────────────────────────────
## Delimiter: ","
## chr (2): Entity, Code
## dbl (2): Year, DALYs (Disability-Adjusted Life Years) - Non-communicable dis...
## 
## ℹ Use `spec()` to retrieve the full column specification for this data.
## ℹ Specify the column types or set `show_col_types = FALSE` to quiet this message.
# Filter the dataset to include only the three countries chosen by your group.
data_ncd_mini <- data_ncd %>%
  filter(Entity %in% c("Botswana", "Brazil", "Brunei"))

# convert column names to lower cases

names(data_ncd_mini) <- tolower(make.names(names(data_ncd_mini)))

#rename daly
data_ncd_mini <- data_ncd_mini %>%
  rename(
    daly = dalys..disability.adjusted.life.years....non.communicable.diseases...sex..both...age..age.standardized..rate.
  )

# rename entity to country
data_ncd_mini <- data_ncd_mini %>%
  rename(
    country = entity
  )

Table of Estimates for NCD Burden Over Time

# Here render a table for the DALY burden over time for the three countries. 
# You should pivot the data wider to show each country in a separate column.

# Pivot to wide format, keeping country and code as identifiers
data_ncd_wide <- data_ncd_mini %>%
  pivot_wider(
    id_cols    = c(country, code),
    names_from = year,
    values_from= daly
  )

# Render the wide table using kable()
knitr::kable(
  data_ncd_wide,
  caption = "DALYs by Country and Year",
  digits = 2,
  align = c("l", "l", rep("r", ncol(data_ncd_wide) - 2))
)
DALYs by Country and Year
country code 1990 1991 1992 1993 1994 1995 1996 1997 1998 1999 2000 2001 2002 2003 2004 2005 2006 2007 2008 2009 2010 2011 2012 2013 2014 2015 2016 2017 2018 2019
Botswana BWA 26190.62 26509.04 27015.22 27528.12 28218.32 28895.38 29361.37 29736.86 30315.12 30743.29 31281.86 31559.43 31665.12 31268.71 30380.28 29328.62 28672.05 27904.02 27746.63 27480.15 27403.35 27281.79 27011.70 26801.49 26616.81 26407.15 26063.55 25947.89 25835.51 25671.73
Brazil BRA 26878.18 26274.16 25980.82 25987.45 25642.50 25320.93 25135.56 24790.31 24678.36 24505.38 24259.14 24054.18 23876.85 23726.83 23577.01 23171.79 22954.68 22684.44 22432.92 22202.47 22005.03 21845.06 21549.33 21327.61 21068.12 20914.63 20920.49 20629.32 20465.81 20309.17
Brunei BRN 29769.74 29192.58 28694.22 28181.41 27201.24 26569.97 25848.91 25972.17 25911.14 25767.12 25561.99 25067.34 24968.82 25113.33 25023.13 24977.85 24710.75 24177.09 23641.58 23597.07 23555.27 23408.48 23299.45 23390.06 23337.90 23250.59 23110.63 22952.47 22834.64 22663.02
# Use kable() from the knitr package to render the table.

Summary of NCD Burden Findings

Provide a brief analysis based on the data presented in the table and chart. Highlight any significant findings or patterns. About 3 sentences.

Between 1990 and 2019, all three countries saw overall declines in non-communicable disease DALYs, with Brazil showing the most consistent year-on-year reduction from about 26 800 to just over 20 000.

Brunei’s DALYs fell from roughly 29 500 to 22 700, though there was a mid-2000s plateau before renewed improvement.

In contrast, Botswana experienced a pronounced rise in DALYs—peaking near 32 000 in the mid-2000s—before reversing course to end at approximately 25 600, mirroring regional gains in disease control but highlighting a later start.